Description

Key Responsibilities

• Lead the design and implementation of enterprise Data Product standards, architecture patterns, and engineering practices within Databricks.
• Partner with business stakeholders, Risk teams, Data Product Owners, and technology leadership to identify high-value data products.
• Define reusable Data Product frameworks, reference architectures, governance controls, metadata standards, and quality requirements.
• Architect and build end-to-end MVP Data Products using Databricks.
• Design data ingestion, transformation, quality, metadata, security, lineage, and consumption layers.
• Develop scalable Lakehouse solutions using Databricks, Delta Lake, Unity Catalog, Spark, and cloud-native services.
• Establish delivery standards supporting product-based data management and domain-oriented ownership.
• Design reusable data product templates, CI/CD patterns, monitoring, observability, and automated testing frameworks.
• Evaluate existing data assets and technology capabilities to identify modernization opportunities.
• Build proof-of-value demonstrations and working MVPs to validate architecture and business value.
• Lead technical workshops, architecture reviews, and discussions around data contracts, interoperability, discoverability, and consumption patterns.
• Provide technical leadership and mentorship to engineering teams.
• Support solution architecture, proposals, effort estimation, and client presentations for Databricks and Data Product transformation initiatives.

Basic Qualifications

• 10+ years of experience in Data Engineering, Data Architecture, Analytics Engineering, or Enterprise Data Management.
• 5+ years designing and implementing modern cloud-based data platforms and data products.
• 4+ years hands-on Databricks experience, including:
  • Delta Lake
  • Unity Catalog
  • Databricks Workflows
  • MLflow
  • Lakehouse Architecture
• 5+ years designing scalable data architectures and distributed data processing solutions.
• 4+ years building enterprise data pipelines using Spark, Python, SQL, and cloud-native technologies.
• 3+ years implementing Data Product operating models, Data Mesh concepts, domain-driven data ownership, or product-oriented data delivery.
• 3+ years delivering cloud solutions using Azure, AWS, or GCP.
• Strong experience with data governance, metadata management, data quality, lineage, and security controls.
• Experience creating reusable architecture patterns, engineering standards, and platform accelerators.
• Experience translating business requirements into scalable data product solutions.
• Strong communication and consulting skills with the ability to lead technical workshops and architecture discussions.

Preferred / Additional Skills

• Data Mesh and domain-driven architecture
• Data Contracts
• Data Product lifecycle management
• Enterprise Data Governance
• Data Quality & Observability
• CI/CD and DevOps
• Cloud Data Architecture
• Risk / Banking data domain experience
• Experience presenting solutions to senior/executive stakeholders

Key Skills

Education

Bachelor’s degree